An Analysis of Black Twitter through Word Clouds (2017)
In order to collect the necessary data for analysis, we designed a twitter data collection program that leverages data streaming functionalities provided by Tweepy, Twitter’s API implemented in python. Once this data is collected, we transition to focusing on the meaning within the tweets. We leverage the Jupyter Notebook platform in order to visualize or data through bar graphs, charts, and clouds. These representations give us a means for making general conclusions about the data, but the focus is determining exactly how masses of people feel about the Oscars. This is done by using python pandas libraries to generate word cloud of the most popular phrases. We separate the tweets based on its native language. We then represent the top five phrases from each language-set through various graphs in hopes to emphasize that the opinions gathered via Twitter are viable and valid to Black sociocultural movements.
Computer Science, C'2018
Associate Professor Computer Science